This disclosure relates generally to the field of digital photography. More particularly, but not by way of limitation, this disclosure relates to still image stabilization techniques. As used herein, image stabilization refers to a collection of techniques for reducing motion-induced blurring during image capture operations. Such motion may result from the movement of the camera, objects in the scene, or both.
Taking high quality photographs in low ambient light conditions, or photographing dynamic scenes (e.g., sport scenes) is challenging due to camera motion and/or the motion of objects within a scene during image capture. One way to reduce motion blur without amplifying an image's noise is to capture and fuse multiple short exposed images of the scene. Such operations are often called ‘Still Image Stabilization.’ While shortening image exposure times can reduce motion blur artifacts, it does so at the expense of a noisier and/or darker image.
A common approach to image stabilization consists of (1) selecting a reference image from a set of multiple short exposed images, (2) globally registering all non-reference images with respect to the reference image, and (3) synthesizing an output image by fusing all captured images to the reference image. In this way the output image represents the scene as it was at the time the reference image was captured, where non-reference images are used to reduce the noise in the reference image by averaging/merging multiple observations of each reference pixel across all images.
A common approach to synthesizing an output image by fusing all registered non-reference images to the reference image is to directly average the images. Direct averaging would reduce the noise in the static areas of the image, but it would also introduce ghosting artifacts. Ghosting artifacts often occur when some of the pixels in the reference image are occluded in some of the non-reference images due to moving objects in the scene. When there is motion between the captured images, significant ghosting artifact can be present in the final output when the images are directly averaged. An example of the effects of such ghosting artifacts is shown in FIG. 1. FIG. 1 shows the resulting output of directly averaging globally registered images. As can be seen from FIG. 1, there are significant ghosting artifacts when images are directly averaged.
One way to avoid ghosting artifacts is for the fusion procedure to distinguish between occlusion and noise and to exclude from fusion all the occluded areas. That can be achieved by excluding from the averaging all non-reference pixels that have very different values in comparison with their corresponding reference pixels. One way to determine the amount of acceptable difference is to calculate it based on the expected noise in a particular pixel. Once the acceptance threshold has been determined, non-reference pixels that differ more than this threshold from their corresponding reference pixels may be excluded from the averaging.
Using a set threshold for ghosting/non-ghosting pixel classification may itself, however, result in image artifacts, particularly in the presence of heavy noise, which may be a typical case for image stabilization. That is because the acceptance threshold is a statistical estimate that may have a certain rate of failure. Neighborhood pixels may easily fall on one side or the other of the threshold, thus creating sudden transitions between ghosting/non-ghosting (i.e. noisier/cleaner) pixels. Thus, currently used fusion methods can be improved.